Overview

Dataset statistics

Number of variables40
Number of observations5000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory354.4 B

Variable types

Numeric9
Categorical31

Alerts

has_secondary_use_gov_office has constant value ""Constant
has_secondary_use_use_police has constant value ""Constant
count_floors_pre_eq is highly overall correlated with height_percentage and 1 other fieldsHigh correlation
height_percentage is highly overall correlated with count_floors_pre_eqHigh correlation
foundation_type is highly overall correlated with roof_type and 3 other fieldsHigh correlation
roof_type is highly overall correlated with foundation_type and 1 other fieldsHigh correlation
ground_floor_type is highly overall correlated with has_superstructure_cement_mortar_brickHigh correlation
other_floor_type is highly overall correlated with count_floors_pre_eq and 1 other fieldsHigh correlation
has_superstructure_mud_mortar_stone is highly overall correlated with foundation_typeHigh correlation
has_superstructure_cement_mortar_brick is highly overall correlated with ground_floor_typeHigh correlation
has_superstructure_rc_non_engineered is highly overall correlated with foundation_typeHigh correlation
has_superstructure_rc_engineered is highly overall correlated with foundation_typeHigh correlation
has_secondary_use is highly overall correlated with has_secondary_use_agriculture and 1 other fieldsHigh correlation
has_secondary_use_agriculture is highly overall correlated with has_secondary_useHigh correlation
has_secondary_use_hotel is highly overall correlated with has_secondary_useHigh correlation
land_surface_condition is highly imbalanced (52.4%)Imbalance
foundation_type is highly imbalanced (60.9%)Imbalance
ground_floor_type is highly imbalanced (59.9%)Imbalance
position is highly imbalanced (50.4%)Imbalance
plan_configuration is highly imbalanced (90.7%)Imbalance
has_superstructure_adobe_mud is highly imbalanced (55.8%)Imbalance
has_superstructure_stone_flag is highly imbalanced (77.5%)Imbalance
has_superstructure_cement_mortar_stone is highly imbalanced (86.2%)Imbalance
has_superstructure_mud_mortar_brick is highly imbalanced (65.3%)Imbalance
has_superstructure_cement_mortar_brick is highly imbalanced (62.4%)Imbalance
has_superstructure_bamboo is highly imbalanced (56.2%)Imbalance
has_superstructure_rc_non_engineered is highly imbalanced (77.1%)Imbalance
has_superstructure_rc_engineered is highly imbalanced (88.4%)Imbalance
has_superstructure_other is highly imbalanced (89.5%)Imbalance
legal_ownership_status is highly imbalanced (85.6%)Imbalance
has_secondary_use_agriculture is highly imbalanced (65.1%)Imbalance
has_secondary_use_hotel is highly imbalanced (79.0%)Imbalance
has_secondary_use_rental is highly imbalanced (93.7%)Imbalance
has_secondary_use_institution is highly imbalanced (99.1%)Imbalance
has_secondary_use_school is highly imbalanced (99.5%)Imbalance
has_secondary_use_industry is highly imbalanced (98.5%)Imbalance
has_secondary_use_health_post is highly imbalanced (99.3%)Imbalance
has_secondary_use_other is highly imbalanced (95.8%)Imbalance
building_id has unique valuesUnique
geo_level_1_id has 92 (1.8%) zerosZeros
age has 540 (10.8%) zerosZeros
count_families has 372 (7.4%) zerosZeros

Reproduction

Analysis started2023-05-13 14:03:21.173929
Analysis finished2023-05-13 14:03:26.623695
Duration5.45 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

building_id
Real number (ℝ)

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean517695.13
Minimum211
Maximum1052595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size207.2 KiB
2023-05-13T16:03:26.656489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum211
5-th percentile51891.25
Q1257904.75
median507432
Q3782577.75
95-th percentile992943.1
Maximum1052595
Range1052384
Interquartile range (IQR)524673

Descriptive statistics

Standard deviation301978.57
Coefficient of variation (CV)0.58331352
Kurtosis-1.1886795
Mean517695.13
Median Absolute Deviation (MAD)261246.5
Skewness0.041247872
Sum2.5884757 × 109
Variance9.1191057 × 1010
MonotonicityNot monotonic
2023-05-13T16:03:26.708929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
802906 1
 
< 0.1%
340888 1
 
< 0.1%
950057 1
 
< 0.1%
325526 1
 
< 0.1%
109058 1
 
< 0.1%
499146 1
 
< 0.1%
648235 1
 
< 0.1%
812124 1
 
< 0.1%
725378 1
 
< 0.1%
917635 1
 
< 0.1%
Other values (4990) 4990
99.8%
ValueCountFrequency (%)
211 1
< 0.1%
459 1
< 0.1%
822 1
< 0.1%
1028 1
< 0.1%
1236 1
< 0.1%
1323 1
< 0.1%
2095 1
< 0.1%
2417 1
< 0.1%
2588 1
< 0.1%
3649 1
< 0.1%
ValueCountFrequency (%)
1052595 1
< 0.1%
1052508 1
< 0.1%
1052121 1
< 0.1%
1051824 1
< 0.1%
1051637 1
< 0.1%
1051267 1
< 0.1%
1050374 1
< 0.1%
1050290 1
< 0.1%
1050285 1
< 0.1%
1049790 1
< 0.1%

geo_level_1_id
Real number (ℝ)

Distinct31
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.7978
Minimum0
Maximum30
Zeros92
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size207.2 KiB
2023-05-13T16:03:26.751649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median12
Q320
95-th percentile27
Maximum30
Range30
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9353834
Coefficient of variation (CV)0.57511947
Kurtosis-1.1866239
Mean13.7978
Median Absolute Deviation (MAD)6
Skewness0.27018242
Sum68989
Variance62.970309
MonotonicityNot monotonic
2023-05-13T16:03:26.788795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
6 471
 
9.4%
17 441
 
8.8%
10 401
 
8.0%
26 392
 
7.8%
7 376
 
7.5%
20 369
 
7.4%
8 356
 
7.1%
21 286
 
5.7%
4 279
 
5.6%
27 234
 
4.7%
Other values (21) 1395
27.9%
ValueCountFrequency (%)
0 92
 
1.8%
1 40
 
0.8%
2 11
 
0.2%
3 147
 
2.9%
4 279
5.6%
5 51
 
1.0%
6 471
9.4%
7 376
7.5%
8 356
7.1%
9 86
 
1.7%
ValueCountFrequency (%)
30 44
 
0.9%
29 3
 
0.1%
28 6
 
0.1%
27 234
4.7%
26 392
7.8%
25 114
 
2.3%
24 27
 
0.5%
23 18
 
0.4%
22 115
 
2.3%
21 286
5.7%

geo_level_2_id
Real number (ℝ)

Distinct987
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean690.168
Minimum1
Maximum1426
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size207.2 KiB
2023-05-13T16:03:26.934746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile68.95
Q1337
median678
Q31030
95-th percentile1376
Maximum1426
Range1425
Interquartile range (IQR)693

Descriptive statistics

Standard deviation410.36269
Coefficient of variation (CV)0.59458376
Kurtosis-1.1648781
Mean690.168
Median Absolute Deviation (MAD)347.5
Skewness0.072768487
Sum3450840
Variance168397.53
MonotonicityNot monotonic
2023-05-13T16:03:26.982003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 67
 
1.3%
158 47
 
0.9%
181 42
 
0.8%
1387 40
 
0.8%
363 37
 
0.7%
673 37
 
0.7%
463 35
 
0.7%
548 35
 
0.7%
157 34
 
0.7%
883 31
 
0.6%
Other values (977) 4595
91.9%
ValueCountFrequency (%)
1 4
 
0.1%
3 1
 
< 0.1%
4 10
0.2%
5 1
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 13
0.3%
10 12
0.2%
11 9
0.2%
13 2
 
< 0.1%
ValueCountFrequency (%)
1426 7
0.1%
1425 2
 
< 0.1%
1422 3
 
0.1%
1421 5
 
0.1%
1420 1
 
< 0.1%
1419 2
 
< 0.1%
1418 2
 
< 0.1%
1416 5
 
0.1%
1415 7
0.1%
1414 14
0.3%

geo_level_3_id
Real number (ℝ)

Distinct3361
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6245.8852
Minimum3
Maximum12561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size207.2 KiB
2023-05-13T16:03:27.030535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile621
Q13007
median6095
Q39501.5
95-th percentile11912.1
Maximum12561
Range12558
Interquartile range (IQR)6494.5

Descriptive statistics

Standard deviation3671.5388
Coefficient of variation (CV)0.58783322
Kurtosis-1.2475764
Mean6245.8852
Median Absolute Deviation (MAD)3237
Skewness0.031648883
Sum31229426
Variance13480197
MonotonicityNot monotonic
2023-05-13T16:03:27.079637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8236 11
 
0.2%
9133 11
 
0.2%
11246 10
 
0.2%
11440 10
 
0.2%
2452 10
 
0.2%
12258 8
 
0.2%
2005 8
 
0.2%
9229 8
 
0.2%
1851 8
 
0.2%
10236 8
 
0.2%
Other values (3351) 4908
98.2%
ValueCountFrequency (%)
3 1
 
< 0.1%
8 1
 
< 0.1%
11 1
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
20 3
0.1%
22 1
 
< 0.1%
24 3
0.1%
26 1
 
< 0.1%
ValueCountFrequency (%)
12561 1
< 0.1%
12553 1
< 0.1%
12532 1
< 0.1%
12528 1
< 0.1%
12527 1
< 0.1%
12526 1
< 0.1%
12512 1
< 0.1%
12511 1
< 0.1%
12508 2
< 0.1%
12494 1
< 0.1%

count_floors_pre_eq
Real number (ℝ)

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1326
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size207.2 KiB
2023-05-13T16:03:27.118338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.73206859
Coefficient of variation (CV)0.34327515
Kurtosis2.8675546
Mean2.1326
Median Absolute Deviation (MAD)0
Skewness0.90569287
Sum10663
Variance0.53592442
MonotonicityNot monotonic
2023-05-13T16:03:27.149133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 3021
60.4%
3 1043
 
20.9%
1 768
 
15.4%
4 124
 
2.5%
5 39
 
0.8%
6 3
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
1 768
 
15.4%
2 3021
60.4%
3 1043
 
20.9%
4 124
 
2.5%
5 39
 
0.8%
6 3
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 1
 
< 0.1%
6 3
 
0.1%
5 39
 
0.8%
4 124
 
2.5%
3 1043
 
20.9%
2 3021
60.4%
1 768
 
15.4%

age
Real number (ℝ)

Distinct27
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.893
Minimum0
Maximum995
Zeros540
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size207.2 KiB
2023-05-13T16:03:27.187045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median15
Q330
95-th percentile60
Maximum995
Range995
Interquartile range (IQR)20

Descriptive statistics

Standard deviation65.777799
Coefficient of variation (CV)2.6424215
Kurtosis196.02818
Mean24.893
Median Absolute Deviation (MAD)10
Skewness13.509181
Sum124465
Variance4326.7189
MonotonicityNot monotonic
2023-05-13T16:03:27.225708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
10 722
14.4%
15 675
13.5%
5 666
13.3%
20 642
12.8%
0 540
10.8%
25 450
9.0%
30 362
7.2%
35 222
 
4.4%
40 176
 
3.5%
50 128
 
2.6%
Other values (17) 417
8.3%
ValueCountFrequency (%)
0 540
10.8%
5 666
13.3%
10 722
14.4%
15 675
13.5%
20 642
12.8%
25 450
9.0%
30 362
7.2%
35 222
 
4.4%
40 176
 
3.5%
45 99
 
2.0%
ValueCountFrequency (%)
995 21
0.4%
190 1
 
< 0.1%
120 4
 
0.1%
115 1
 
< 0.1%
110 4
 
0.1%
105 2
 
< 0.1%
100 28
0.6%
95 3
 
0.1%
90 15
0.3%
85 22
0.4%

area_percentage
Real number (ℝ)

Distinct45
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0978
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size207.2 KiB
2023-05-13T16:03:27.267127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median7
Q310
95-th percentile16
Maximum100
Range99
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.6058743
Coefficient of variation (CV)0.56878094
Kurtosis45.950263
Mean8.0978
Median Absolute Deviation (MAD)2
Skewness4.2072904
Sum40489
Variance21.214078
MonotonicityNot monotonic
2023-05-13T16:03:27.311929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
6 806
16.1%
7 689
13.8%
5 615
12.3%
8 584
11.7%
9 400
8.0%
4 364
7.3%
10 295
 
5.9%
11 275
 
5.5%
3 218
 
4.4%
12 147
 
2.9%
Other values (35) 607
12.1%
ValueCountFrequency (%)
1 5
 
0.1%
2 61
 
1.2%
3 218
 
4.4%
4 364
7.3%
5 615
12.3%
6 806
16.1%
7 689
13.8%
8 584
11.7%
9 400
8.0%
10 295
 
5.9%
ValueCountFrequency (%)
100 1
 
< 0.1%
56 1
 
< 0.1%
55 1
 
< 0.1%
51 1
 
< 0.1%
50 1
 
< 0.1%
47 1
 
< 0.1%
42 1
 
< 0.1%
40 1
 
< 0.1%
39 1
 
< 0.1%
38 5
0.1%

height_percentage
Real number (ℝ)

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4454
Minimum2
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size207.2 KiB
2023-05-13T16:03:27.350445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q36
95-th percentile9
Maximum32
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9249311
Coefficient of variation (CV)0.35349674
Kurtosis11.986289
Mean5.4454
Median Absolute Deviation (MAD)1
Skewness1.6747551
Sum27227
Variance3.7053599
MonotonicityNot monotonic
2023-05-13T16:03:27.380137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
5 1571
31.4%
6 865
17.3%
4 714
14.3%
7 642
12.8%
3 475
 
9.5%
8 271
 
5.4%
2 185
 
3.7%
9 112
 
2.2%
10 93
 
1.9%
11 22
 
0.4%
Other values (7) 50
 
1.0%
ValueCountFrequency (%)
2 185
 
3.7%
3 475
 
9.5%
4 714
14.3%
5 1571
31.4%
6 865
17.3%
7 642
12.8%
8 271
 
5.4%
9 112
 
2.2%
10 93
 
1.9%
11 22
 
0.4%
ValueCountFrequency (%)
32 1
 
< 0.1%
26 1
 
< 0.1%
18 1
 
< 0.1%
16 3
 
0.1%
15 7
 
0.1%
13 16
 
0.3%
12 21
 
0.4%
11 22
 
0.4%
10 93
1.9%
9 112
2.2%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
t
4196 
n
636 
o
 
168

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt
2nd rowo
3rd rowt
4th rowt
5th rowt

Common Values

ValueCountFrequency (%)
t 4196
83.9%
n 636
 
12.7%
o 168
 
3.4%

Length

2023-05-13T16:03:27.414679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:27.449242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
t 4196
83.9%
n 636
 
12.7%
o 168
 
3.4%

Most occurring characters

ValueCountFrequency (%)
t 4196
83.9%
n 636
 
12.7%
o 168
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5000
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 4196
83.9%
n 636
 
12.7%
o 168
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 5000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 4196
83.9%
n 636
 
12.7%
o 168
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 4196
83.9%
n 636
 
12.7%
o 168
 
3.4%

foundation_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
r
4202 
w
 
312
u
 
274
i
 
185
h
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowr
2nd rowr
3rd rowr
4th rowr
5th rowr

Common Values

ValueCountFrequency (%)
r 4202
84.0%
w 312
 
6.2%
u 274
 
5.5%
i 185
 
3.7%
h 27
 
0.5%

Length

2023-05-13T16:03:27.478889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:27.517247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
r 4202
84.0%
w 312
 
6.2%
u 274
 
5.5%
i 185
 
3.7%
h 27
 
0.5%

Most occurring characters

ValueCountFrequency (%)
r 4202
84.0%
w 312
 
6.2%
u 274
 
5.5%
i 185
 
3.7%
h 27
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5000
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 4202
84.0%
w 312
 
6.2%
u 274
 
5.5%
i 185
 
3.7%
h 27
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 5000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 4202
84.0%
w 312
 
6.2%
u 274
 
5.5%
i 185
 
3.7%
h 27
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 4202
84.0%
w 312
 
6.2%
u 274
 
5.5%
i 185
 
3.7%
h 27
 
0.5%

roof_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
n
3534 
q
1164 
x
 
302

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rown
2nd rown
3rd rown
4th rown
5th rown

Common Values

ValueCountFrequency (%)
n 3534
70.7%
q 1164
 
23.3%
x 302
 
6.0%

Length

2023-05-13T16:03:27.547834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:27.582815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
n 3534
70.7%
q 1164
 
23.3%
x 302
 
6.0%

Most occurring characters

ValueCountFrequency (%)
n 3534
70.7%
q 1164
 
23.3%
x 302
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5000
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 3534
70.7%
q 1164
 
23.3%
x 302
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 3534
70.7%
q 1164
 
23.3%
x 302
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 3534
70.7%
q 1164
 
23.3%
x 302
 
6.0%

ground_floor_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
f
4043 
v
477 
x
453 
z
 
19
m
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowf
2nd rowx
3rd rowf
4th rowf
5th rowf

Common Values

ValueCountFrequency (%)
f 4043
80.9%
v 477
 
9.5%
x 453
 
9.1%
z 19
 
0.4%
m 8
 
0.2%

Length

2023-05-13T16:03:27.613112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:27.650754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
f 4043
80.9%
v 477
 
9.5%
x 453
 
9.1%
z 19
 
0.4%
m 8
 
0.2%

Most occurring characters

ValueCountFrequency (%)
f 4043
80.9%
v 477
 
9.5%
x 453
 
9.1%
z 19
 
0.4%
m 8
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5000
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 4043
80.9%
v 477
 
9.5%
x 453
 
9.1%
z 19
 
0.4%
m 8
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 5000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 4043
80.9%
v 477
 
9.5%
x 453
 
9.1%
z 19
 
0.4%
m 8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 4043
80.9%
v 477
 
9.5%
x 453
 
9.1%
z 19
 
0.4%
m 8
 
0.2%

other_floor_type
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
q
3175 
x
832 
j
751 
s
 
242

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowq
2nd rowq
3rd rowx
4th rowx
5th rowx

Common Values

ValueCountFrequency (%)
q 3175
63.5%
x 832
 
16.6%
j 751
 
15.0%
s 242
 
4.8%

Length

2023-05-13T16:03:27.685549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:27.722341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
q 3175
63.5%
x 832
 
16.6%
j 751
 
15.0%
s 242
 
4.8%

Most occurring characters

ValueCountFrequency (%)
q 3175
63.5%
x 832
 
16.6%
j 751
 
15.0%
s 242
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5000
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
q 3175
63.5%
x 832
 
16.6%
j 751
 
15.0%
s 242
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 5000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
q 3175
63.5%
x 832
 
16.6%
j 751
 
15.0%
s 242
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
q 3175
63.5%
x 832
 
16.6%
j 751
 
15.0%
s 242
 
4.8%

position
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
s
3868 
t
830 
j
 
266
o
 
36

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt
2nd rows
3rd rowt
4th rows
5th rows

Common Values

ValueCountFrequency (%)
s 3868
77.4%
t 830
 
16.6%
j 266
 
5.3%
o 36
 
0.7%

Length

2023-05-13T16:03:27.756198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:27.795467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
s 3868
77.4%
t 830
 
16.6%
j 266
 
5.3%
o 36
 
0.7%

Most occurring characters

ValueCountFrequency (%)
s 3868
77.4%
t 830
 
16.6%
j 266
 
5.3%
o 36
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5000
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 3868
77.4%
t 830
 
16.6%
j 266
 
5.3%
o 36
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 5000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 3868
77.4%
t 830
 
16.6%
j 266
 
5.3%
o 36
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 3868
77.4%
t 830
 
16.6%
j 266
 
5.3%
o 36
 
0.7%
Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
d
4804 
q
 
90
u
 
80
s
 
8
c
 
5
Other values (5)
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowd
2nd rowd
3rd rowd
4th rowd
5th rowd

Common Values

ValueCountFrequency (%)
d 4804
96.1%
q 90
 
1.8%
u 80
 
1.6%
s 8
 
0.2%
c 5
 
0.1%
a 5
 
0.1%
m 3
 
0.1%
o 3
 
0.1%
n 1
 
< 0.1%
f 1
 
< 0.1%

Length

2023-05-13T16:03:27.829976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:27.873421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
d 4804
96.1%
q 90
 
1.8%
u 80
 
1.6%
s 8
 
0.2%
c 5
 
0.1%
a 5
 
0.1%
m 3
 
0.1%
o 3
 
0.1%
n 1
 
< 0.1%
f 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
d 4804
96.1%
q 90
 
1.8%
u 80
 
1.6%
s 8
 
0.2%
c 5
 
0.1%
a 5
 
0.1%
m 3
 
0.1%
o 3
 
0.1%
n 1
 
< 0.1%
f 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5000
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 4804
96.1%
q 90
 
1.8%
u 80
 
1.6%
s 8
 
0.2%
c 5
 
0.1%
a 5
 
0.1%
m 3
 
0.1%
o 3
 
0.1%
n 1
 
< 0.1%
f 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 5000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 4804
96.1%
q 90
 
1.8%
u 80
 
1.6%
s 8
 
0.2%
c 5
 
0.1%
a 5
 
0.1%
m 3
 
0.1%
o 3
 
0.1%
n 1
 
< 0.1%
f 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 4804
96.1%
q 90
 
1.8%
u 80
 
1.6%
s 8
 
0.2%
c 5
 
0.1%
a 5
 
0.1%
m 3
 
0.1%
o 3
 
0.1%
n 1
 
< 0.1%
f 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4541 
1
459 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 4541
90.8%
1 459
 
9.2%

Length

2023-05-13T16:03:27.911739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:27.946997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4541
90.8%
1 459
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 4541
90.8%
1 459
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4541
90.8%
1 459
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4541
90.8%
1 459
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4541
90.8%
1 459
 
9.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
1
3805 
0
1195 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 3805
76.1%
0 1195
 
23.9%

Length

2023-05-13T16:03:27.975523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:28.009636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 3805
76.1%
0 1195
 
23.9%

Most occurring characters

ValueCountFrequency (%)
1 3805
76.1%
0 1195
 
23.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3805
76.1%
0 1195
 
23.9%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3805
76.1%
0 1195
 
23.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3805
76.1%
0 1195
 
23.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4819 
1
 
181

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4819
96.4%
1 181
 
3.6%

Length

2023-05-13T16:03:28.038962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:28.073267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4819
96.4%
1 181
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 4819
96.4%
1 181
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4819
96.4%
1 181
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4819
96.4%
1 181
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4819
96.4%
1 181
 
3.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4903 
1
 
97

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4903
98.1%
1 97
 
1.9%

Length

2023-05-13T16:03:28.105404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:28.142741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4903
98.1%
1 97
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 4903
98.1%
1 97
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4903
98.1%
1 97
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4903
98.1%
1 97
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4903
98.1%
1 97
 
1.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4675 
1
 
325

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4675
93.5%
1 325
 
6.5%

Length

2023-05-13T16:03:28.172374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:28.206767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4675
93.5%
1 325
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 4675
93.5%
1 325
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4675
93.5%
1 325
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4675
93.5%
1 325
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4675
93.5%
1 325
 
6.5%

has_superstructure_cement_mortar_brick
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4636 
1
 
364

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4636
92.7%
1 364
 
7.3%

Length

2023-05-13T16:03:28.235008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:28.267968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4636
92.7%
1 364
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 4636
92.7%
1 364
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4636
92.7%
1 364
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4636
92.7%
1 364
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4636
92.7%
1 364
 
7.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
3720 
1
1280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 3720
74.4%
1 1280
 
25.6%

Length

2023-05-13T16:03:28.297073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:28.330713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3720
74.4%
1 1280
 
25.6%

Most occurring characters

ValueCountFrequency (%)
0 3720
74.4%
1 1280
 
25.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3720
74.4%
1 1280
 
25.6%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3720
74.4%
1 1280
 
25.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3720
74.4%
1 1280
 
25.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4547 
1
 
453

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 4547
90.9%
1 453
 
9.1%

Length

2023-05-13T16:03:28.360314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:28.394006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4547
90.9%
1 453
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0 4547
90.9%
1 453
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4547
90.9%
1 453
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4547
90.9%
1 453
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4547
90.9%
1 453
 
9.1%

has_superstructure_rc_non_engineered
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4814 
1
 
186

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4814
96.3%
1 186
 
3.7%

Length

2023-05-13T16:03:28.422591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:28.456560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4814
96.3%
1 186
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 4814
96.3%
1 186
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4814
96.3%
1 186
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4814
96.3%
1 186
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4814
96.3%
1 186
 
3.7%

has_superstructure_rc_engineered
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4922 
1
 
78

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4922
98.4%
1 78
 
1.6%

Length

2023-05-13T16:03:28.485034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:28.517728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4922
98.4%
1 78
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 4922
98.4%
1 78
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4922
98.4%
1 78
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4922
98.4%
1 78
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4922
98.4%
1 78
 
1.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4931 
1
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%

Length

2023-05-13T16:03:28.547216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:28.580201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4931
98.6%
1 69
 
1.4%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
v
4810 
a
 
100
w
 
61
r
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowv
2nd rowv
3rd rowv
4th rowv
5th rowv

Common Values

ValueCountFrequency (%)
v 4810
96.2%
a 100
 
2.0%
w 61
 
1.2%
r 29
 
0.6%

Length

2023-05-13T16:03:28.607563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:28.643644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
v 4810
96.2%
a 100
 
2.0%
w 61
 
1.2%
r 29
 
0.6%

Most occurring characters

ValueCountFrequency (%)
v 4810
96.2%
a 100
 
2.0%
w 61
 
1.2%
r 29
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5000
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 4810
96.2%
a 100
 
2.0%
w 61
 
1.2%
r 29
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
v 4810
96.2%
a 100
 
2.0%
w 61
 
1.2%
r 29
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
v 4810
96.2%
a 100
 
2.0%
w 61
 
1.2%
r 29
 
0.6%

count_families
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9834
Minimum0
Maximum5
Zeros372
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size207.2 KiB
2023-05-13T16:03:28.670288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.39440531
Coefficient of variation (CV)0.40106296
Kurtosis15.18297
Mean0.9834
Median Absolute Deviation (MAD)0
Skewness1.3430566
Sum4917
Variance0.15555555
MonotonicityNot monotonic
2023-05-13T16:03:28.702011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 4384
87.7%
0 372
 
7.4%
2 210
 
4.2%
3 26
 
0.5%
4 5
 
0.1%
5 3
 
0.1%
ValueCountFrequency (%)
0 372
 
7.4%
1 4384
87.7%
2 210
 
4.2%
3 26
 
0.5%
4 5
 
0.1%
5 3
 
0.1%
ValueCountFrequency (%)
5 3
 
0.1%
4 5
 
0.1%
3 26
 
0.5%
2 210
 
4.2%
1 4384
87.7%
0 372
 
7.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4439 
1
561 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4439
88.8%
1 561
 
11.2%

Length

2023-05-13T16:03:28.734522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:28.767866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4439
88.8%
1 561
 
11.2%

Most occurring characters

ValueCountFrequency (%)
0 4439
88.8%
1 561
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4439
88.8%
1 561
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4439
88.8%
1 561
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4439
88.8%
1 561
 
11.2%

has_secondary_use_agriculture
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4673 
1
 
327

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4673
93.5%
1 327
 
6.5%

Length

2023-05-13T16:03:28.798241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:28.831722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4673
93.5%
1 327
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 4673
93.5%
1 327
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4673
93.5%
1 327
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4673
93.5%
1 327
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4673
93.5%
1 327
 
6.5%

has_secondary_use_hotel
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4834 
1
 
166

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4834
96.7%
1 166
 
3.3%

Length

2023-05-13T16:03:28.860054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:28.893902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4834
96.7%
1 166
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 4834
96.7%
1 166
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4834
96.7%
1 166
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4834
96.7%
1 166
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4834
96.7%
1 166
 
3.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4963 
1
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4963
99.3%
1 37
 
0.7%

Length

2023-05-13T16:03:28.922415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:28.956106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4963
99.3%
1 37
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 4963
99.3%
1 37
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4963
99.3%
1 37
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4963
99.3%
1 37
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4963
99.3%
1 37
 
0.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4996 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4996
99.9%
1 4
 
0.1%

Length

2023-05-13T16:03:28.984007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:29.018037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4996
99.9%
1 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 4996
99.9%
1 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4996
99.9%
1 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4996
99.9%
1 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4996
99.9%
1 4
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4998 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4998
> 99.9%
1 2
 
< 0.1%

Length

2023-05-13T16:03:29.048296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:29.082964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4998
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 4998
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4998
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4998
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4998
> 99.9%
1 2
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4993 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4993
99.9%
1 7
 
0.1%

Length

2023-05-13T16:03:29.112791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:29.149561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4993
99.9%
1 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 4993
99.9%
1 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4993
99.9%
1 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4993
99.9%
1 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4993
99.9%
1 7
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4997 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4997
99.9%
1 3
 
0.1%

Length

2023-05-13T16:03:29.178082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:29.213741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4997
99.9%
1 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 4997
99.9%
1 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4997
99.9%
1 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4997
99.9%
1 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4997
99.9%
1 3
 
0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
5000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5000
100.0%

Length

2023-05-13T16:03:29.358820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:29.391668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 5000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5000
100.0%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
5000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5000
100.0%

Length

2023-05-13T16:03:29.418502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:29.450258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 5000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5000
100.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
0
4977 
1
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4977
99.5%
1 23
 
0.5%

Length

2023-05-13T16:03:29.476803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:29.511038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4977
99.5%
1 23
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 4977
99.5%
1 23
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4977
99.5%
1 23
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4977
99.5%
1 23
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4977
99.5%
1 23
 
0.5%

damage_grade
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size207.2 KiB
2
2866 
3
1646 
1
488 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
2 2866
57.3%
3 1646
32.9%
1 488
 
9.8%

Length

2023-05-13T16:03:29.538793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-13T16:03:29.573872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2 2866
57.3%
3 1646
32.9%
1 488
 
9.8%

Most occurring characters

ValueCountFrequency (%)
2 2866
57.3%
3 1646
32.9%
1 488
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2866
57.3%
3 1646
32.9%
1 488
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2866
57.3%
3 1646
32.9%
1 488
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2866
57.3%
3 1646
32.9%
1 488
 
9.8%

Interactions

2023-05-13T16:03:25.913883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-13T16:03:23.306850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.664964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.036257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.399120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.859671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.209225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.559756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.951337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:22.993052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.346670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.705555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.075865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.439226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.897741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.247873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.597727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.990866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.032552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.385710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.746151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.115903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.480251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.937515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.287844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.637471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:26.030368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.073735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.428102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.788173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.159983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.620713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.977515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.328386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.679199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:26.069872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.112970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.468212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.828992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.199186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.661175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.017366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.368458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.719801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:26.110384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.154789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.509744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.875851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.242344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.703530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.058092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.408765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.761014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:26.146929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.191357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.547691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.914800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.280420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.742909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.094868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.446585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.799217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:26.185574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.229379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.586250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.955381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.320143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.781962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.132193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.483106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.837442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:26.223055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.268451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.625209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:23.996031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.358862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:24.821496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.171631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.522034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-13T16:03:25.875531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-13T16:03:29.622532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
building_idgeo_level_1_idgeo_level_2_idgeo_level_3_idcount_floors_pre_eqagearea_percentageheight_percentagecount_familiesland_surface_conditionfoundation_typeroof_typeground_floor_typeother_floor_typepositionplan_configurationhas_superstructure_adobe_mudhas_superstructure_mud_mortar_stonehas_superstructure_stone_flaghas_superstructure_cement_mortar_stonehas_superstructure_mud_mortar_brickhas_superstructure_cement_mortar_brickhas_superstructure_timberhas_superstructure_bamboohas_superstructure_rc_non_engineeredhas_superstructure_rc_engineeredhas_superstructure_otherlegal_ownership_statushas_secondary_usehas_secondary_use_agriculturehas_secondary_use_hotelhas_secondary_use_rentalhas_secondary_use_institutionhas_secondary_use_schoolhas_secondary_use_industryhas_secondary_use_health_posthas_secondary_use_otherdamage_grade
building_id1.0000.0100.025-0.0160.0120.003-0.002-0.003-0.0020.0000.0000.0270.0090.0160.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0160.0000.0390.0120.0000.0000.0120.0140.0210.0000.0000.0000.0000.014
geo_level_1_id0.0101.000-0.068-0.002-0.084-0.0540.033-0.0810.0390.0550.2030.2010.0970.1170.1240.0400.2700.3330.1030.0590.2600.1930.2280.2580.0540.0630.0740.0970.1120.1260.0250.0070.0000.0000.0000.0000.0260.260
geo_level_2_id0.025-0.0681.000-0.0060.0510.043-0.0130.039-0.0030.0350.0960.0600.0690.0650.0620.0260.0880.1530.0610.0320.1090.1000.0840.0860.0310.0580.0000.0840.0000.0340.0000.0360.0000.0000.0000.0000.0360.076
geo_level_3_id-0.016-0.002-0.0061.000-0.010-0.0030.021-0.020-0.0090.0360.0190.0170.0000.0100.0000.0000.0420.0310.0440.0110.0430.0170.0360.0290.0390.0000.0000.0490.0000.0370.0000.0000.0230.0000.0000.0000.0000.000
count_floors_pre_eq0.012-0.0840.051-0.0101.0000.2740.1170.7550.0740.0660.1520.1880.1320.5820.3080.1840.1950.3600.0440.0000.4110.2500.1090.0840.0980.1980.0180.0440.0740.0580.1650.0390.0000.0520.0390.0840.0000.157
age0.003-0.0540.043-0.0030.2741.000-0.0220.2200.0880.0140.0200.0030.0350.0240.1340.0260.0810.0850.0000.0000.1790.0000.0000.0230.0090.0030.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.024
area_percentage-0.0020.033-0.0130.0210.117-0.0221.0000.2040.0530.0190.1670.2420.1680.1770.0460.1910.0000.2510.0000.1060.0510.2160.0270.0000.1780.2450.0100.0060.1300.0190.1820.1000.0000.1780.0000.0650.0000.109
height_percentage-0.003-0.0810.039-0.0200.7550.2200.2041.0000.0390.0380.1720.2600.1290.3050.2180.1850.1450.2740.0000.0530.2570.1560.0560.0410.2050.2680.0000.0000.1300.0000.2270.0680.0000.1180.0000.2010.0000.093
count_families-0.0020.039-0.003-0.0090.0740.0880.0530.0391.0000.0000.0640.0990.0540.0690.0460.0000.0340.0610.0000.0410.0660.0800.0200.0260.0700.1150.0000.0290.1230.0490.1060.0960.0000.0000.0000.0000.0000.076
land_surface_condition0.0000.0550.0350.0360.0660.0140.0190.0380.0001.0000.0180.0320.0580.0500.0460.0000.0290.0840.0530.0220.0600.0550.0530.0000.0000.0130.0270.0240.0000.0210.0300.0060.0290.0000.0000.0000.0110.026
foundation_type0.0000.2030.0960.0190.1520.0200.1670.1720.0640.0181.0000.5330.3560.4000.1050.0720.1030.5530.1540.2130.0690.4970.3700.3230.5110.5240.0590.1540.1680.0510.2620.1810.0300.0980.0350.0410.0320.312
roof_type0.0270.2010.0600.0170.1880.0030.2420.2600.0990.0320.5331.0000.4730.5290.1320.0440.0660.4220.0370.0800.0340.3710.1500.0960.4650.4830.0240.0390.1660.0500.2660.1640.0160.0320.0330.0590.0000.249
ground_floor_type0.0090.0970.0690.0000.1320.0350.1680.1290.0540.0580.3560.4731.0000.3700.0930.0370.0780.4880.1160.1590.0760.5730.1050.0860.3600.3700.0000.0280.1710.0630.2890.1470.0330.0550.1410.0460.0580.260
other_floor_type0.0160.1170.0650.0100.5820.0240.1770.3050.0690.0500.4000.5290.3701.0000.1310.0480.0910.4400.1150.1080.0260.4210.1900.0800.4060.3870.0290.0590.2020.0610.3070.1650.0180.0850.0590.0690.0300.244
position0.0000.1240.0620.0000.3080.1340.0460.2180.0460.0460.1050.1320.0930.1311.0000.0520.1880.2900.0140.0250.3340.1130.0700.0840.1150.1070.0000.0140.1530.0220.2580.0850.0000.0320.0550.0230.0000.050
plan_configuration0.0000.0400.0260.0000.1840.0260.1910.1850.0000.0000.0720.0440.0370.0480.0521.0000.0080.1260.0170.0220.0930.0640.0380.0450.0000.1180.1600.0590.0390.0000.0330.0000.0590.3130.0000.0400.0000.087
has_superstructure_adobe_mud0.0000.2700.0880.0420.1950.0810.0000.1450.0340.0290.1030.0660.0780.0910.1880.0081.0000.3230.0110.0000.2630.0430.0000.0000.0280.0340.0120.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.064
has_superstructure_mud_mortar_stone0.0000.3330.1530.0310.3600.0850.2510.2740.0610.0840.5530.4220.4880.4400.2900.1260.3231.0000.0000.0920.3480.4450.0380.0460.2180.2190.0000.1340.0740.0680.1660.0900.0000.0190.0330.0050.0000.339
has_superstructure_stone_flag0.0180.1030.0610.0440.0440.0000.0000.0000.0000.0530.1540.0370.1160.1150.0140.0170.0110.0001.0000.0360.0180.0460.1400.0660.0120.0140.0440.0050.0000.0000.0050.0000.0000.0000.0000.0000.0000.060
has_superstructure_cement_mortar_stone0.0000.0590.0320.0110.0000.0000.1060.0530.0410.0220.2130.0800.1590.1080.0250.0220.0000.0920.0361.0000.0000.0450.0210.0000.0820.0000.0030.0000.0370.0000.0400.0000.0160.0000.0000.0000.0180.064
has_superstructure_mud_mortar_brick0.0000.2600.1090.0430.4110.1790.0510.2570.0660.0600.0690.0340.0760.0260.3340.0930.2630.3480.0180.0001.0000.0000.0000.0090.0340.0190.0000.0080.0000.0250.0560.0000.0000.0000.0000.0000.0000.067
has_superstructure_cement_mortar_brick0.0000.1930.1000.0170.2500.0000.2160.1560.0800.0550.4970.3710.5730.4210.1130.0640.0430.4450.0460.0450.0001.0000.0580.0590.1090.0910.0000.0680.0730.0420.1470.0780.0000.0000.0150.0000.0000.285
has_superstructure_timber0.0000.2280.0840.0360.1090.0000.0270.0560.0200.0530.3700.1500.1050.1900.0700.0380.0000.0380.1400.0210.0000.0581.0000.4570.0390.0710.0850.1180.0150.0000.0410.0000.0000.0000.0000.0000.0000.073
has_superstructure_bamboo0.0000.2580.0860.0290.0840.0230.0000.0410.0260.0000.3230.0960.0860.0800.0840.0450.0000.0460.0660.0000.0090.0590.4571.0000.0000.0340.0780.0840.0180.0000.0430.0050.0000.0000.0000.0000.0080.078
has_superstructure_rc_non_engineered0.0160.0540.0310.0390.0980.0090.1780.2050.0700.0000.5110.4650.3600.4060.1150.0000.0280.2180.0120.0820.0340.1090.0390.0001.0000.0000.0000.0000.1050.0000.1780.0360.0000.0000.0000.0090.0000.209
has_superstructure_rc_engineered0.0000.0630.0580.0000.1980.0030.2450.2680.1150.0130.5240.4830.3700.3870.1070.1180.0340.2190.0140.0000.0190.0910.0710.0340.0001.0000.0000.0000.1150.0190.1340.1670.0000.1180.0000.0260.0000.231
has_superstructure_other0.0390.0740.0000.0000.0180.0000.0100.0000.0000.0270.0590.0240.0000.0290.0000.1600.0120.0000.0440.0030.0000.0000.0850.0780.0000.0001.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.010
legal_ownership_status0.0120.0970.0840.0490.0440.0000.0060.0000.0290.0240.1540.0390.0280.0590.0140.0590.0550.1340.0050.0000.0080.0680.1180.0840.0000.0000.0001.0000.0370.0150.0900.0000.0000.0000.0220.0000.0700.077
has_secondary_use0.0000.1120.0000.0000.0740.0000.1300.1300.1230.0000.1680.1660.1710.2020.1530.0390.0000.0740.0000.0370.0000.0730.0150.0180.1050.1150.0000.0371.0000.7430.5190.2390.0670.0380.0960.0540.1860.064
has_secondary_use_agriculture0.0000.1260.0340.0370.0580.0150.0190.0000.0490.0210.0510.0500.0630.0610.0220.0000.0000.0680.0000.0000.0250.0420.0000.0000.0000.0190.0000.0150.7431.0000.0450.0110.0000.0000.0000.0000.0580.063
has_secondary_use_hotel0.0120.0250.0000.0000.1650.0000.1820.2270.1060.0300.2620.2660.2890.3070.2580.0330.0000.1660.0050.0400.0560.1470.0410.0430.1780.1340.0100.0900.5190.0451.0000.0000.0000.0000.0000.0000.0000.111
has_secondary_use_rental0.0140.0070.0360.0000.0390.0000.1000.0680.0960.0060.1810.1640.1470.1650.0850.0000.0000.0900.0000.0000.0000.0780.0000.0050.0360.1670.0000.0000.2390.0110.0001.0000.0000.0000.0000.0000.0000.082
has_secondary_use_institution0.0210.0000.0000.0230.0000.0000.0000.0000.0000.0290.0300.0160.0330.0180.0000.0590.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0670.0000.0000.0001.0000.0000.0000.0000.0000.000
has_secondary_use_school0.0000.0000.0000.0000.0520.0000.1780.1180.0000.0000.0980.0320.0550.0850.0320.3130.0000.0190.0000.0000.0000.0000.0000.0000.0000.1180.0000.0000.0380.0000.0000.0000.0001.0000.0000.0000.0000.000
has_secondary_use_industry0.0000.0000.0000.0000.0390.0000.0000.0000.0000.0000.0350.0330.1410.0590.0550.0000.0000.0330.0000.0000.0000.0150.0000.0000.0000.0000.0000.0220.0960.0000.0000.0000.0000.0001.0000.0000.0000.016
has_secondary_use_health_post0.0000.0000.0000.0000.0840.0000.0650.2010.0000.0000.0410.0590.0460.0690.0230.0400.0000.0050.0000.0000.0000.0000.0000.0000.0090.0260.0000.0000.0540.0000.0000.0000.0000.0000.0001.0000.0000.012
has_secondary_use_other0.0000.0260.0360.0000.0000.0000.0000.0000.0000.0110.0320.0000.0580.0300.0000.0000.0000.0000.0000.0180.0000.0000.0000.0080.0000.0000.0000.0700.1860.0580.0000.0000.0000.0000.0000.0001.0000.000
damage_grade0.0140.2600.0760.0000.1570.0240.1090.0930.0760.0260.3120.2490.2600.2440.0500.0870.0640.3390.0600.0640.0670.2850.0730.0780.2090.2310.0100.0770.0640.0630.1110.0820.0000.0000.0160.0120.0001.000

Missing values

2023-05-13T16:03:26.311208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-13T16:03:26.519084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

building_idgeo_level_1_idgeo_level_2_idgeo_level_3_idcount_floors_pre_eqagearea_percentageheight_percentageland_surface_conditionfoundation_typeroof_typeground_floor_typeother_floor_typepositionplan_configurationhas_superstructure_adobe_mudhas_superstructure_mud_mortar_stonehas_superstructure_stone_flaghas_superstructure_cement_mortar_stonehas_superstructure_mud_mortar_brickhas_superstructure_cement_mortar_brickhas_superstructure_timberhas_superstructure_bamboohas_superstructure_rc_non_engineeredhas_superstructure_rc_engineeredhas_superstructure_otherlegal_ownership_statuscount_familieshas_secondary_usehas_secondary_use_agriculturehas_secondary_use_hotelhas_secondary_use_rentalhas_secondary_use_institutionhas_secondary_use_schoolhas_secondary_use_industryhas_secondary_use_health_posthas_secondary_use_gov_officehas_secondary_use_use_policehas_secondary_use_otherdamage_grade
080290664871219823065trnfqtd11000000000v1000000000003
1288308900281221087ornxqsd01000000000v1000000000002
29494721363897321055trnfxtd01000000000v1000000000003
3590882224181069421065trnfxsd01000011000v1000000000002
420194411131148833089trnfxsd10000000000v1000000000003
53330208558608921095trnfqsd01000000000v1110000000002
672845194751206622534nrnxqsd01000000000v1000000000003
747551520323122362086twqvxsu00000110000v1000000000001
84411260757721921586trqfqsd01000010000v1000000000002
99895002688699410134tinvjsd00000100000v1000000000001
building_idgeo_level_1_idgeo_level_2_idgeo_level_3_idcount_floors_pre_eqagearea_percentageheight_percentageland_surface_conditionfoundation_typeroof_typeground_floor_typeother_floor_typepositionplan_configurationhas_superstructure_adobe_mudhas_superstructure_mud_mortar_stonehas_superstructure_stone_flaghas_superstructure_cement_mortar_stonehas_superstructure_mud_mortar_brickhas_superstructure_cement_mortar_brickhas_superstructure_timberhas_superstructure_bamboohas_superstructure_rc_non_engineeredhas_superstructure_rc_engineeredhas_superstructure_otherlegal_ownership_statuscount_familieshas_secondary_usehas_secondary_use_agriculturehas_secondary_use_hotelhas_secondary_use_rentalhas_secondary_use_institutionhas_secondary_use_schoolhas_secondary_use_industryhas_secondary_use_health_posthas_secondary_use_gov_officehas_secondary_use_use_policehas_secondary_use_otherdamage_grade
4990195225211426104992054nrnfxsd01000000000v1000000000002
499131163426392005215277tixvstd00000000100v1101000000002
499288612961210123721065trnfqsd01000000000v1000000000003
4993403506644948412093trnfjsd01000000000v1000000000002
4994792506275486560220146ornfqsd01000000000v1000000000003
499541540691151617912032trnfjsd01000000000v1000000000002
49962647241010551194521056trqfqtd01000000000v1000000000002
4997452094734831332565trnfqsd01000000000v0110000000002
499881429411660963648068trnfqod00001010000v1000000000003
4999891577637611743240136trqfqsd01000000000v1110000000002